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Hauben M. A Pharmacovigilance Florilegium. Clin Ther 2024; 46:520-523. [PMID: 39030077 DOI: 10.1016/j.clinthera.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/21/2024]
Affiliation(s)
- Manfred Hauben
- Department of Family and Community Medicine, New York Medical College, Valhalla, New York; Truliant Consulting, Baltimore, Maryland.
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Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions. Clin Ther 2023; 45:117-133. [PMID: 36732152 DOI: 10.1016/j.clinthera.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/15/2022] [Accepted: 01/09/2023] [Indexed: 02/01/2023]
Abstract
Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.
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Hauben M. Artificial intelligence in pharmacovigilance: Do we need explainability? Pharmacoepidemiol Drug Saf 2022; 31:1311-1316. [PMID: 35747938 DOI: 10.1002/pds.5501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/27/2022] [Accepted: 06/08/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Manfred Hauben
- Pfizer Inc., New York, New York, USA.,NYU Langone Health, New York, New York, USA
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Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022; 28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
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Affiliation(s)
- Imogen S Stafford
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University Of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research, University HospitalSouthampton, Southampton, UK
| | | | - Enrico Mossotto
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Sarah Ennis
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Manfred Hauben
- Pfizer Inc, New York, NY, USA
- NYU Langone Health, Department of Medicine, New York, NY, USA
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Gavriilidis GI, Dimitriadis VK, Jaulent MC, Natsiavas P. Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach. Drug Saf 2021; 44:1165-1178. [PMID: 34674190 PMCID: PMC8553681 DOI: 10.1007/s40264-021-01103-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2021] [Indexed: 12/02/2022]
Abstract
Introduction Information technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These ‘intelligent’ technical paradigms provide a new digital ‘toolkit’ supporting drug safety and healthcare processes, including ‘active pharmacovigilance’. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation. Objective We dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm. Results To this end, we elaborate on the related ‘business processes’ (i.e. operational processes) and ‘user goals’ identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond. Conclusions We highlight the fact that beyond providing analytics and useful statistics to the end user, ‘actionability’ has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.
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Affiliation(s)
- George I. Gavriilidis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
| | - Vlasios K. Dimitriadis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France
| | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France
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Hauben M, Hartford CG. Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider. Clin Ther 2021; 43:372-379. [PMID: 33478803 DOI: 10.1016/j.clinthera.2020.12.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/13/2020] [Accepted: 12/19/2020] [Indexed: 12/22/2022]
Abstract
Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the scope of artificial intelligence in pharmacovigilance (AIPV) may be advantageous to more sharply defining, for example, which terms, methods, tasks, and data sets are suitably subsumed under the application of AIPV. Accordingly, this article explores relevant points to consider regarding defining the scope of AIPV and offers a potential working definition of the scope of AIPV.
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Affiliation(s)
- Manfred Hauben
- Safety Sciences Research, Pfizer Inc, New York, NY, USA; Department of Medicine, NYU Langone Health, New York, NY, USA
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Hauben M, Gregory WW, Caubel P. Response to "Pharmacovigilance 2030: Invited Commentary for the January 2020 ‘Futures’ Edition". Clin Pharmacol Ther 2020; 108:28. [PMID: 32196646 PMCID: PMC7383961 DOI: 10.1002/cpt.1812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 01/29/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Manfred Hauben
- Worldwide Safety Pfizer Inc New York City New York USA
- Department of Medicine New York University Langone Health New York City New York USA
| | | | - Patrick Caubel
- Worldwide Safety Pfizer Inc Collegeville Pennsylvania USA
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Stergiopoulos S, Fehrle M, Caubel P, Tan L, Jebson L. Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey. Pharmaceut Med 2020; 33:499-510. [PMID: 31933240 DOI: 10.1007/s40290-019-00307-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Drug safety remains a top global public health concern. An increase in the number of data sources available has increased the complexity of pharmacovigilance operations, so the US FDA has created draft guidance focusing on optimizing drug safety data for well-characterized medicines. However, to date, no data demonstrating changes in reports have been presented. OBJECTIVES This study provided data assessing changes in individual case safety reports (ICSRs) and aggregate reports (ARs) for large biopharmaceutical companies from 2007 to 2017. This study also evaluated current trends on the use of advanced machine and deep learning in order to process all data captured for ICSRs as well as opinions from industry thought leaders on creating a sustainable case-processing operation. METHODOLOGY Using data captured from Navitas Life Science's annual pvnet® benchmark, we calculated workload indicators characterizing pharmacovigilance operations for large biopharmaceutical organizations. Workload indicators included the number of ICSRs by organization, the number of ARs, and the number and types of data sources used. We also conducted structured in-depth interviews with seven biopharmaceutical executives to discover the reasons for changes in workload indicators across time as well as current strategies for increasing efficiencies in drug safety reporting. RESULTS The median number of ICSRs increased from 84,960 cases in 2007 to over 200,000 cases in 2017; this increase was largely attributable to an increase in both nonserious cases and follow-up cases. Member companies reported using 12 ± 3 data sources for case identification. The number of ARs also increased from a median of 70 reports in 2007 to 258 reports in 2017. To address these increases, 61% of the biopharmaceutical organizations we surveyed planned to adopt machine learning for full ICSR processing; however, as of 2018, none of the organizations surveyed had mechanisms in place. CONCLUSION This study demonstrated that pharmacovigilance departments are currently burdened by ever-increasing case volumes. With increased guidance from regulatory agencies, as well as improvements in artificial intelligence and natural language processing, biopharmaceutical organizations must determine the most resource-efficient and sustainable methods to process the growing volume of cases.
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Affiliation(s)
- Stella Stergiopoulos
- Tufts Center for the Study of Drug Development, Tufts University School of Medicine, 75 Kneeland Street, Ste 1100, Boston, MA, 02111, USA.
| | | | | | - Louise Tan
- Pvnet®, Navitas Life Sciences GmbH, 60528, Frankfurt, Germany
| | - Louise Jebson
- Pvnet®, Navitas Life Sciences GmbH, 60528, Frankfurt, Germany
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Bate A, Hornbuckle K, Juhaeri J, Motsko SP, Reynolds RF. Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance. Ther Adv Drug Saf 2019; 10:2042098619864744. [PMID: 31428307 PMCID: PMC6683315 DOI: 10.1177/2042098619864744] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Andrew Bate
- Division of Translational Medicine, Department of Medicine, NYU School of Medicine, 462 1st Avenue, NY10016, New York, USA
| | - Ken Hornbuckle
- Global Patient Safety, Eli Lilly and Company, Indianapolis, IN, USA
| | - Juhaeri Juhaeri
- Juhaeri Juhaeri, Medical Evidence Generation, Sanofi US, Bridgewater, NJ, USA
| | | | - Robert F. Reynolds
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
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Koutkias V. From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data. Drug Saf 2019; 42:583-586. [PMID: 30666591 DOI: 10.1007/s40264-018-00793-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou-Thermi Road, Thermi, P.O. Box 60631, 57001, Thessaloniki, Greece.
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Pharmacovigilance and Some Thoughts About What We Eat. Clin Ther 2018; 40:1957-1961. [PMID: 30545607 DOI: 10.1016/j.clinthera.2018.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 11/02/2018] [Indexed: 11/20/2022]
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Pharmacovigilance: Challenges in Getting From Here to There. Clin Ther 2018; 40:1964-1966. [PMID: 30473400 DOI: 10.1016/j.clinthera.2018.10.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 10/24/2018] [Accepted: 10/25/2018] [Indexed: 11/24/2022]
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